Haas Newsroom

A profitable holiday shopping season depends on manufacturers
providing retailers with incentives to forecast demand, says UC business
professor

Retailers may not have predicted an official recession for this year’s
holiday shopping season, but they can make better future forecasting decisions,
according to Terry Taylor, associate professor at UC Berkeley’s Haas
School of Business.

“The current economy points to the importance or value of knowing what
the market will look like when you’ll actually plan to sell the
product, “ says Taylor. Taylor’s research suggests strategies
for both manufacturers and retailers to optimize profit. First, retailers should
invest in forecasting to better predict demand. Second, manufacturers, by offering
a “menu” of purchasing agreements, can encourage retailers to invest
in forecasting demand.

In the working paper, “Incentives for Retailer Forecasting: Rebates versus
Returns,” Taylor and co-author Wenqiang Xiao, assistant professor, Stern
School of Business, New York University, find that when a retailer’s
forecasting efforts are taken into account, contracts allowing retailers to
return orders are more effective than those involving rebate offers.

Taylor describes returns and rebates as “mirror images.” Return
policies allow retailers to return unsold goods to the manufacturer for a pre-determined
credit. Rebates, on the other hand, pay the retailer a bonus every time
she sells a unit of merchandise. “A rebate compensates the retailer for
selling units, whereas returns essentially compensate the retailer for not selling
units,” says Taylor.

“It’s natural to think that rebates might be the better way to
go if a manufacturer is trying to encourage a retailer to invest in forecasting.
After all, a returns policy is basically an insurance policy, which means the
retailer doesn’t have to worry as much about what the actual demand will
end up being,” says Taylor. However, the study finds placing orders with
a return policy is more effective for the supplier.

“The trick is to offer a menu of choices,” says Taylor. For
example, a manufacturer gives the retailer the option to purchase either at
a high unit price coupled with a generous return credit or, the option to buy
at a low unit price coupled with a stingy return credit. Retailers who have
done their forecasting homework benefit from this “menu” because
they can choose the option that best fits with their sense of market demand.
Using pre-season testing to forecast demand can be expensive but Taylor emphasizes, “When
there is a downturn in the economy, there is less room for error.”

The paper’s conclusions are based on an analytical model that captures
the retailer’s forecasting effort, contract selection, and ordering decisions.
The paper offers advice to retailers about how much time and resources they
should devote to forecasting demand given a certain set of contractual terms.
The value of having the better demand information depends on the terms that
are available to the retailer. Taylor says retailers can benefit by thinking
about the interaction between the terms of trade and their forecasting efforts.

Finally, the paper acknowledges that manufacturers might not always want to
encourage their retailers to invest in forecasting. “When a retailer
invests in forecasting, she obtains a strategic advantage over the manufacturer
through the private information she obtains, and this can end up hurting the
manufacturer,” says Taylor. However, even when the manufacturer wants
to discourage forecasting efforts, the paper shows that offering returns is
more effective than offering rebates.

Taylor is a member of Haas’ Operations and Information Technology Management
Group.